The present invention relates generally to a patient monitor. More particularly, the present invention relates to a system, method and software program product for analyzing video frames and determining the fall risk state of a patient by discriminating non-motion noise and non-dangerous motion from patient movements.
Fall reduction has become a major focus of all healthcare facilities, including those catering to permanent residents. Healthcare facilities invest a huge amount of their resources in falls management programs and assessing the risk of falls in a particular patient class, location, and care state, along with the risk factors associated with significant injuries. Recent studies have found fall rates at 2.2 to 7.0 (per 1000 patient bed days) in acute care hospitals, 11.0 to 24.9 in long-term care hospitals, and 8.0 to 19.8 in rehabilitation hospitals, with the majority of falls occurring from, or near, the patient's bed, usually by patients who should not ambulate without assistance. The patient's mental status is most often listed as the most common risk factor contributing to falls. Bed falls can account for up to half of all falls in a healthcare facility. The range of injury from all falls has been reported to be at a rate of 29 to 48 percent, with 4 percent to 7.5 percent resulting in serious injury to the patient. The intention of these studies is to improve patient care by providing adequate monitoring programs corresponding to the perceived patient risk and injury. Realistically, however, it is simply impossible to know for sure which patient will fall, and the severity of the injury that may result from any fall. Bed falls have received an extensive amount of scrutiny due to the patient's high expectation of safety and the disproportional potential for severe injury to the patient over other types of falls.
Round the clock patient monitoring by a staff nurse is expensive, therefore, healthcare facilities have investigated alternatives in order to reduce the monitoring staff, while increasing patient safety. In the past, patients at risk of falling from their beds were either physically restrained or sedated, regardless of the patient's mental status. Both of these preventives are now considered to be measures of last resort that are reserved for unruly or incompetent patients. Presently, falls prevention is subdivided into intervention and monitoring techniques. Interventions are aimed at minimizing falls risk and include such measures as ensuring that the patient can reach necessary items from the bed, ensuring that the bed is in a low position and the bed brakes are locked, ensuring that the patient has a manual bed call button within reach for summoning a nurse and that a charge nurse responds (albeit verbally) to every call. Other interventions include the use of half length bedrails to reduce the patient's need to climb over rails to exit the bed and keeping the bedside area uncluttered and obstacle free. Perhaps the most easily implemented intervention is clear instructions from an attending nurse to request assistance prior to leaving the bed.
Healthcare facilities rely on patient monitoring to supplement interventions and reduce the instances of patient falls. Eyes-on monitoring of patients is problematic for two reasons, cost and privacy. Most facilities maximize the patient-to-nurse staffing ratios by care units, e.g., recovery and critical care units have a lower patient-to-nurse staffing ratio than floor bed units, and, typically, bed patients demand greater privacy than those in critical or special care units. For these reasons, patient monitoring has relied on technological solutions rather than nurse monitoring. Note however, that these solutions are alerting devices, used as an aid for patient care and are not a substitute for adequate quality staffing.
Prior art fall prevention monitors include alarms using pressure sensitive pads or position sensitive transmission patches. The first type of fall prevention monitor uses a pressure sensitive pad that senses the patient's body mass. If the sensor detects a change in the patient's body mass, a remotely located alarm is sounded to summon staff. These monitors are extremely adaptable and may be placed in one or more locations on the patient's bed, on the floor adjacent to the bed, on chairs, toilet seats, wheel chairs and almost any other place that a patient may rest. These devices have gained considerable acceptance in the healthcare industry because they are relatively inexpensive, non-intrusive, exhibit a fairly low instance of false alarms and are reliable. These monitors can be used in tandem to more accurately assess the position of a patient, thereby further reducing false alarms. For instance, the system may be configured with one pressure sensitive pad in the bed and under the patient and a second pressure sensitive pad on the floor. Then, the staff will be alerted whenever a patient's weight shifts off of the bed pad and again when the patient's weight is sensed by the floor pad.
Detractors to pressure sensitive fall prevention monitors counter that these types of devices may be more accurately described as “patient fall detectors” than “fall prevention monitors” because they typically alert only after a fall has occurred and the patient's weight has shifted out of the bed. In other words, prior art pressure sensitive fall monitors cannot perceive that the patient is in the process of getting out of bed, only that the patient's weight has left the bed. Additionally, poorly placed pressure sensitive pads may send multiple nuisance alarms that must be responded to and then to reposition the pressure sensitive pad requires that the bed be empty.
More recently, patient position sensitive transmission patches have been introduced that sense the position of a body part and send an alarm if the part is in a “near weight bearing position.” The “patch” is a battery powered inclinometer, processing circuitry and a transmitter enclosed in an adhesive patch that is used in conjunction with a wireless receiver and alarm. The patch may be applied to the back of a patient's thigh parallel with the femur. Whenever the patient's thigh is approaching a weight bearing angle, the patch sends an alert signal to the remote receiver and an alarm sounds. These position sensitive transmission patches are relatively inexpensive and can be worn continuously for up to three weeks. The downside is battery life. The transmission patch device is essentially in a sleep mode when the patch is horizontal and consumes relatively little power, however when the patch is oriented off horizontal, the inclinometer and associate electronics are continuously processing measurements. Alert transmissions consume even more battery power.
Recently however, video programs have been introduced for alerting healthcare professionals of a patient's fall from a video surveillance system. Many patient rooms now contain video surveillance equipment for monitoring and recording activity in a patient's room. Typically, these video systems compare one video frame with a preceding frame for changes in the video frames that exceed a certain threshold level. These may be taking from the color, luminance or any other attribute of the video frame. If the condition is met, the system automatically flags the frame as having motion and may save it for further review or archive, or alternatively it may alert the healthcare professional to potential patient movement in the patient's room. More advanced systems identify particular zones within the patient room that are associated with a potential hazard for the patient. Then, sequential video frames are evaluated for changes in those zones. If the system detects a change in one of the zones, the system assumes that the patient has entered the zone. Typically, these zones are associated with areas within the patient's room that are associated with a patient fall, such as the floor or lavatory area, or near a sink, toilet, chair or bed where a patient may land after a fall. In those systems, the healthcare professional is immediately alerted if the motion is detected in the danger zones and this is indicative of a patient fall. The healthcare professional can then assist the patient to his feet or summon emergency assistance as necessary. While these systems alleviate the healthcare professional of some of the burden of monitoring the patient, they suffer from the shortcoming of being essentially reactive, alerting the staff only after an event as occurred.
An improved video monitoring system is disclosed in U.S. patent application Ser. No. 12/151,452 filed May 6, 2008, entitled System and Method for Predicting Patient Falls. That system differs from the prior art in that it analyzes the surveillance video frames for patterns of motion. It is essentially a two-tier method. First, it evaluates changes between video frames in a patient's room for patterns that could only be attributed to the patient, thus it discriminates motion in the room, or motion that is not the patient moving. Second, the predictive system attempts to identify patient movement that is a precursor to a patient fall. Certain movements by the patient have no predictive value to the system and are, therefore, discarded from further evaluation. Other patient movement may indicate that the patient will attempt a dangerous movement, one that might result in a patient fall. This movement is not necessarily a fall, but movement that will likely precede a fall. Thus, it is not necessarily that the patient has been detected moving, or where in the room the motion is detected, but how and where that patient movement relates to other motion attributed to the patient. This is achieved by recognizing patterns of patient movements that are precursor movements to a patient fall. Essentially, a plurality of patient movement signatures are compiled and saved in the system. Then, once the system has detected motion in the patient room that may be the patient moving around, the character of that patient movement is saved. As more patient movements are detected, the character of those movements is saved with the previously detected patient movement. Patterns of patient movement accrued over time are compared to the patient movement signatures. If the movement pattern compares favorably to a signature that has been identified as a precursor to a patient fall, the healthcare professional is immediately alerted, who then intervenes before the patient falls. Typically, the healthcare professional audibly warns the patient of the danger and simultaneously hurries to the patient's room.
While the predictive patient fall system has been demonstrated to be extremely useful in reducing the frequency of patient falls in a healthcare facility, generally the system errs on the side of caution. Recognizing patient movement that is predictive of a patient fall is valuable if the movement patterns correlate to patient movement signatures that occur well in advance of the fall, at least with sufficient time for the healthcare professional to successfully intervene. Furthermore, movement signatures that do not necessarily result in a patient fall are of extreme importance to the system, even though the likelihood of a fall is somewhat remote. Once the patient traverses the final stages of a fall, there is much less opportunity of the healthcare professional to successfully intervene and far less likely of a satisfactory outcome. Hence, the library of patient movement signatures compiled with many movement signatures that will almost never be satisfied, i.e., compare favorable to a pattern of patient movement detected by the system. In many cases, the system will recognize two or more patterns of patient movement from the surveillance video that partially match one or more fall prediction movement signatures. Hence, any changes detected between video frames that complete a movement pattern that matches a fall signature will result in an alert being sent to the healthcare professional.
As should be appreciated, because the predictive system may have identified multiple patient movement patterns that are similar, but not a match to a fall signature, any changes between video frames that complete one of the movement patterns matching a fall signature will result in an alert, regardless of the source of the changes. Any patient movement that completes a movement pattern matching a fall signature must be investigated by an attending healthcare professional. For the most part, these patient movements are merely the patient attempting to do something that requires assistance, such as getting out of a chair, out of a tub, out of bed, off a gurney, etc., regardless of whether or not the patient intends to make such a movement or not. Typically, the video fall prediction system may issue a plurality of alerts to the attending healthcare professionals for patient movement that never culminates in a patient attempt to do something that requires assistance. Regardless, each of these alerts must be investigated by the attending healthcare professional. However, other events may occur that are not related to patient movement that may also be interpreted, incorrectly, as a change in the video that completes one or more patient movement patterns matching a fall prediction movement signature. Typically, these “false alarms” are attributed to spurious noise in the system. Noise may result from electrical noise on electrical components, light noise on light receptors, or some combination of the two. These false alarms greatly increase the amount of work for healthcare professionals charged with patient care above the number of valid alerts that must be investigated by the professional. The continued or frequent issuance of false alarms ultimately results in healthcare professionals being less vigilant in their patient monitoring and response to fall alerts.